As a commodity, wind energy is typically traded in ex-ante time frames and is dependent on forecasts to secure physical positions up to 36 hours after gate closure for trading in Day-Ahead markets. Wind energy is traded in discrete quantities, however it is generated from an intermittent and variable resource. Deterministic forecasts are preferred for energy trading as the most compatible solution to provide a defined forecast quantity. However, deterministic forecasts cannot capture the stochastic nature of the underlying power source and are therefore sub-optimal. Ensemble based forecasts have the potential to reduce forecast error by accounting for uncertainties not captured in deterministic models. However, ensemble forecasts are not always available at the vertical levels at which wind turbines operate. Therefore, a method is needed to apply ensemble information to turbine hub heights for energy forecasting purposes. This paper presents a novel machine learning based method that translates the perturbations from a localised Numerical Weather Prediction model’s 10m wind speed component to an ensemble energy forecast at 100m. The extrapolated ensemble based forecast has improved the forecast accuracy by 9% when compared to the deterministic output. The findings will have important implications for future energy trading, transmission system operation and meteorological forecasting.

As a commodity, wind energy is typically traded in ex-ante time frames and is dependent on forecasts to secure physical positions up to 36 hours after gate closure for trading in Day-Ahead markets. Wind energy is traded in discrete quantities, however it is generated from an intermittent and variable resource. Deterministic forecasts are preferred for energy trading as the most compatible solution to provide a defined forecast quantity. However, deterministic forecasts cannot capture the stochastic nature of the underlying power source and are therefore sub-optimal. Ensemble based forecasts have the potential to reduce forecast error by accounting for uncertainties not captured in deterministic models. However, ensemble forecasts are not always available at the vertical levels at which wind turbines operate. Therefore, a method is needed to apply ensemble information to turbine hub heights for energy forecasting purposes. This paper presents a novel machine learning based method that translates the perturbations from a localised Numerical Weather Prediction model’s 10m wind speed component to an ensemble energy forecast at 100m. The extrapolated ensemble based forecast has improved the forecast accuracy by 9% when compared to the deterministic output.  The findings will have important implications for future energy trading, transmission system operation and meteorological forecasting.